{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train:  (49000,)\n",
      "y_val:  (1000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "y_test:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.21214764175e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "# x_shape = (1,3,5,5)\n",
    "# w_shape = (2,3,3,3)\n",
    "# x = np.array([[[1,0,2,1,2],[0,0,0,2,2],[0,2,0,0,0],[1,2,0,2,1],[0,2,2,0,1]],|\n",
    "#               [[1,0,2,0,1],[2,1,1,2,2],[0,0,1,0,1],[0,1,0,2,2],[0,2,2,2,0]],\n",
    "#               [[1,1,1,1,1],[2,2,0,1,1],[2,1,0,1,0],[2,1,2,2,1],[0,2,1,2,0]]])\n",
    "# x = x.reshape((1,3,5,5))\n",
    "# w = np.array([[[[0,1,0],[1,-1,-1],[0,-1,0]],\n",
    "#               [[1,1,0],[0,1,-1],[1,0,-1]],\n",
    "#               [[1,-1,0],[1,-1,-1],[1,1,-1]]],\n",
    "#               [[[0,1,1],[0,1,0],[-1,-1,-1]],\n",
    "#               [[0,-1,1],[1,-1,0],[1,-1,0]],\n",
    "#               [[0,1,0],[1,0,0],[-1,-1,-1]]]\n",
    "#              ])\n",
    "# b = np.array([1,0])\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "# print (out)\n",
    "#Compare your output to ours; difference should be around 2e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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y3KIx4EQIMfcZgbJCg1pnIke4iBQcQ4M/GC04K/lXIsOJ5Pb3E5LAQuTAYkiT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mKLCTZLMYmhdBOyrCQpLfVmQ5Mbbj64w+nTpxzanOAMWZlgLjoy23t1XOqYTaec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\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KjzUCn8nEFInx/2fvXZakR5J7v797BIDMrKqvq7une6gzh9JCG70Hn0MrbbnQE9CMT6AX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QKIbTQaY7FYLHB4sI+DwyNHt/nhF26Rmt6grCJM9gbI8hRXizVs1aCYG4g4hc1z1GsDU+1DxAlq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obo6+vD6/Wq5ohRNVEUGVutlirKjPLMEmOkIHU6naRSKWZmZgCd8zMwMMCBAwcIBoO7vlNA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1kwaeGHD+4v1l40/e8xfbnPhsYe+Fv7e8rzMnfZ9gC98Pqutoyp0KDEh3hmbo+J6Pt6hQ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th2abNO4n+f1tkpY2VqTKTGkHb46GNSWDic1uaRvtLbfh95NMaOU8aOtZFa15iYf2JMpMi14wsl\nL3GMWhhzNIy8l8JO4nlTSkl6vuHZOjmT9gQP24wxMdfFO4ILGOfjNmsxpcX5DG8cxkYmlHmWg4me\n2erQ8dGPf0y5uuJQVzGROF/ROUPf9pg8kOV5JDOwAecb8hAHQ4bBWIvjGL3JC4sPHucHznk6ggkY\na8gJsY4OQ8Ke6/DGgfH4ItA1HT0dtrRkQ2FPAHtxgW8aro3Fdz2h65mZgrqoWc476rah7XsWBPJZ\nSVZE76w3FctiTpmXzIoyelu9wTqYlYbSwUVZMi9nLC4jrj7D0HU9xXLG9XLG4XDgxV/8GevNPfP5\nnMJkrGYLDocDl7MFbd9xc3HJtopFxazQRbaQU+AaHw1PO4fM4cOetmsxJqOclaPBJB6jtm3HF/B9\nMXjenOr823YY9homcizBOagrfFvjqx1919PiORjP3XpDazL6Ys71fMld1XOxWJKFjDzEcUbwWAJW\nWFZUsdvHJjpyJe9zmjB5blIVI0BjrPVx+vM5xUN/n1oo9OQOxwVW2iwKpiyIkkcSQuD58+djfk+a\nf6YZgHQ0Ry80WinSzEjaABCjTH+XfYUoIV2s5X6fPXs25iHqGhfL5XJUMEQREdH5UsJGJxEXkYuL\ni5jbpwwX3TYpWOq95/r6ejQUBYorOT+SPyTXFCNNJ91rClmdFyD3qaNImvntMYse21qplGiiGNer\n1erE2BHvuSYqeDJ2vl/Rz0ZH0fQcJHOdVjLn8/lJnR1xDIjCLpE62V/20+dLx/6JfjShCMt+qSEs\nImNFIOt6XtJzohhrMqZ0HSD97sn1NWmDRIA0yctqtRodKbLu63lXomG6YGaaUyP9kUaRZJvM25oM\nQT837WDSBs/7oodoeds7rMcBHJ+THj96u6b9hjcpykXOGT3vGul5NMaOljSik/6e7pvu/7bj3rZf\nfADnvbYhHCvPy0sQfMANcBBRPFxpRsagsixHmEqR5xhrIzGBnNSHiDpjIB0w8pB99LaHgOuOPOXe\nuZElLoQQqbDPiAnK4g4AFmNCNKaCI9hITABElrkixxQ5M++xdUXuBu9nkWOzjNl8Tr5YULUN3jBE\nlgp2A1yl7ZSndZgIlsslTVXhQ89+W0MYFsg5ZCYql7O8oDHNqHDsq8PYl5J8HA1h5XWtIuOcnnxF\nAUo9EHrB+DBEc7Z5IEZm2i5CAcx+z+2rbyEvqG4OlNWMxoYIS7SQmQwTItLRGEXZ+4iNHXgTuiWL\nbDo5n4vcyP568p2K7Gh5yAOmF4cpiIBcSyuPshjKAiH1dBaLxbiowpuLto7epG2T7ZpBJ61ppc+j\nj0nvI+0H8ahKdFjaPZvNRmNEiuppY8n7WO19vV6P0RXZX9onhT/FkJLIjUS84BjFCSGMELgQwkhg\nopOVU2NFagelz0VH0+Wfhgh+CIq9XvO04SdR9e12O9ZTm8/nY9/qJG6t1KSKyYcz1/5x5JzRmOor\n6fwhf9MoBhzXQ1kTjTFjREK2awNm6trpfJKStej3ZEppTceFPodcX4wIcWhoOJg26ORYrTRPKdDS\n5tlsdgLBNMaczE8SWdaOEE2dD6dQOY0okblTR5KEKEKfR/fDubn5fZHv6rjQ+8l8Kp+dcyf9L0ah\nHKejYalu9l0jPY/G2JlSJs55TPW4SPdPz6ePEw3OGBNpe88YPFmsXX9y/qNYYixo2J9oFvnBOHHO\nUXctru1YLC7GF/bq4nJsk7UWYy3W2LENIYRo10TNM/5uVIFUH6M1xwHhOOkINS7SF+dk8hkKlWYm\nx1qo+hpvLRRFNHSMYW4spfc0VU2fW0zX4YYEdjsoJLQdtijxHozJyPMaMzAjNdvdSPE4m82YlxHC\nVzYdFIG2bui6qJgYG8979+o1F1eXzMsc78vRaOz7HrJ8ZJmZzWbj710XC512nRsiX8VQZLR7g1pT\n/qae9ccrfvzfMNyjGuPOOdq64bDbUl3vaasD3XLJIcSwfVkMOGJryIzkfMUzhvB4jZ2peQPOV8oW\nechYSaM86f7nDKFzc9NDbReFUxYMiSSIkSAQlNSrK8dr72R67SljZ2o/fQ/puzM6HIbfxTEhcBCJ\nUOkok0RVdZ0agbGFEKjr+kSR0IUupRL61dVVdJgMBYThmGcic0EIgcPhMG7XFNbicRVIHRxrBB0O\nh7FtqQdbKyt6PKS/PWZJDTwR6V/JVRJmSzgq1tJXaTTuSX43eWieOKff6O167Mp7IMq4dmycq/Oi\nDYL0vKkXXr8P8j11Mp2TKaejNoAk+jN1/xpOB0c4lERUJG9GFw7VtPi6IKkcJ4WD08iO6DHiiJH5\nTfefRJ30/JFGy3TfpNTT76s+8i7v80PrpuRXpcx2Mi+/Dfb6Xfvl0Rg7IlMvwJsL78PH6OP08UZH\naya2A28YOmJcjS+nGYwbAhnDQHWyLcOFWLPgcDjw+fyS3f2GP/vLvxgX9SuBcPhAMOEI1wN8iHk2\ngcGIMmFUOgP+5MXBhBgFCtN9IAaSsLVZIozPE+GAQSYWl0EWCHi8iZ7ZrCzJgT7LyDNL6DqyEOgl\nUXq5hKoavRnW5BT5gdl8CVjatqdvO+q65vLyMib4Abk1ZEUB8yVbv6eqKrb3G2aLSG+9Wd9j84zl\nfM5uUEDquoY8JgdKscX5Ysl+v6fu2mGyCvgAvXOE8KbXWeSxJxOfExnPBAfe4pqa/W7D3AfaumK3\nvuP1UEhteXHBrOuGZ2mwGIIFEzJC8ANhwuPtoykDRURHbFLFIP13zhjQC/JDRs3JvJPMYWnkJG27\nVrK7rqMsyzEXR6BV4h1Li1zqdsr2dEGZgl6li3zqNdXGjj6fXrR1hEoULb3Q6fwebezoYpXz+fwk\nb6RtWw6Hw+ghFJIDOVaKfu52O6Sg4m63A2JUTO7jcDiMhs7d3d3YNu/jfC1eyLRPdKRr6pl9SJLe\nV9u2Y79KhEeeWyzevBrH41RU4EOIfL2vkvatfk9FNJGGvGNCTKDHszZm0t/h1MOu90kdAVMOGN1e\nrQSnSn4aSQdOIJP6elOJ7mKQiNNDQ0zbth1he/p9l7ZI1Hm5XI5zjCjkEpnWdYh0P0uukRhFDzlE\ndL++rwbOd5XUwE2fvQQAZC7V66esa3IOvc7Isd9FHo2x85CR81C0Z+rYs9tCGCFdgTPnmrq+8nSP\nHvSxPZaQBYwo2SYb/3355Zf87d/+7QivmC1i8mtW5BF2FgaFzBj8MD7cGDVKMKMCZ2PMVGL8giTj\nD8f4MNBSe4KLk6AZ79OCiVElgwGbE0zAWxPhS1lOyAaYXdGTOU+w8YU2XT9gbkvmswHuY3J6F8jL\nYvSeilFSHyqaqoaLS5bLJcsiGjCXiwXe9eAdTd3GSE9TUbfR87RYLcFacmuo6wP3+3rkxd/strRd\nDHH3ziH5WgDORTNRPCtT9XQ+FEXFn+T2DOMzWCBA2+JtRZ3lHPZb8jKGkS2B57OSpp/H+kVugCAF\nMGbIyxhP+Tgn46kFJYVTPBQen5p30uMeMnLOzVHp+bSk0DVNBCALad/37Ha70UOpkzzFi6uVzqnz\na9FRLq1IyGKjF23x0MlnndSrz6ehJhpOI9FYY8yYW6MjAvL55uZmdKBIe/b7/WjQ3NzcHGuTwWjU\nSDslv0kzipVlOUaWttvtGAUCRir6FFqin7P8m3KefWgyta4KXHiz2XB5ecnt7S1wzKOS/pOxoaMJ\nv2/C8ZM8LFPPS+fi6fVvNpuNNblSY0eOS/P3HroOHKNAqdE0Jfp3ia6k71n6OY1+aGU5LVys85JS\nSFTTNCMEU9hbxaEizhchT5GaYNKPOn9S1+NJ+0T3nd42FWX/UOcP4A0DVu5ZPoszDBjhgJJLmeY7\npRG8t8mjMXbSBVkWoNSjOkZX1DY4fk+Tik+Sdf3DPmsbYsQmPTeBGIkZDJ2AwYSAB8pZSXAeZy0B\nyywYnuclV32Laxy/+Nf/b8xZsbuM1WpF20fcuB28LXmeY7KoLPR2CrvIyb1YM7TRHS1hmxdAJE8w\nPuD6jtA7Ag7j+jGEPeJiTbyxmRmSxmwg5IZ8oCnug2e5uiLLSlzb0TVt7CDnaXYNxWJO71rK+YKP\n5pFUoK0bfB+LkeY2sifdfvsS17d8/vnn/PiTj9lut3HAE1jNSuqm419/+2v6usX3HVXbsN7cR2Nl\nYAjLy4JXd6/puo4iL7nfaRYkYeiLeSepF1p78uVF/BAmHL2m+DDA18wAbcxy8I7QVKxffoWhB1fT\n1zuy3vPs2TOc+5TlbE5Z5MyKjFlRUhbZwDII58uZPsmTPMmTPMmTPMmTvD/yaIwdmM6zmbKM08/6\ne/r7ueu8YSwNhtA5+Jumpo6/WwgObyw+9BgstsjJBrxo23sYjAvvPU3XQhe9Ax8Xn0QlHnCDIZeb\nWL9nDBFzNHgyDNaY6I2PMSlsMANJwcBSMtTgMSFC3nA+wpqEPc6HaBxlkfxACAIyLCZYrIEsy8my\nAmMtmXh6vcG5gOtidMh1Ece9zKN3pm/6mOAeoiW+Wq2w1rLb7SLu3vds7gKr+YJVXlJkFrKcMs8j\nrjYzMbm4jaHiMs/pfaAeImDR+2pjEVYX2LcV4lQWJ4IE26QGj3gap6BKj93IETl/FwOpRdcSrMH0\nPX1dU9stszJnvV1TlBmHOlawDyGG6w0DtMA4MiNgyscr+t2eimycGwdT0LN0nnnbvlPHyf5p5ASO\nURHBN0t4X44R2uX1es18PqfrujHyI5XsBealIyxy77oPNFxAt0t+E1iGiI7qaOIPje3XTqYpr6tm\n4RFvs9A2i3cvhDDCTLRXuq5rXr58Od77p59++kZtDPGoSvRLRBMPACMjU+rR1v00tXakET2RD2E+\n0eupFhmrkrdTluVYCw0ipbiQUmgSCZEpKNOT/GFERx/1+z0F4xXPuo6e6vdb3j94k8Xy3PNM2eDO\njaX0GD3HpdGhFA6p2yCRbBlvAl8T5kX5rOmlJZK0WCxO+kVYI6V/ZH7QfVOW5dhGPX9oSds7Nfd/\n6O9Duu7JWJN5VshhZLuGY+uI0O/aT4/G2DHJP2RwpCHBEPMTHgoPnhtYekHTkaMsYZ9KDSHv/Rvt\niOQCOUVmyMoc1/W4rseaDGtjNOLlb7+iaYd6MUNdmdVqdYJTNEppyLIMF3oiG3DMkfHek2PJyAgG\n+uEecmsjYcHwPbOevmnJchujK95hTaBrI4TO990AZYtwl8wWtK4nwzIvS7bVISarD8wkMlk6FyM1\nWZbRD5NJ27a0d3dcXV1B8HS9GyE0Qmv5/OaGw27Ddtuw2Wzouo5Pnr/gYrnk9XpN33aUeU7TteQ2\nG3G5u6rCY9jtdti8oKoqdv0wKRJrcVRVjcCF9WNOMfeyWKfbHruCAm/mrcUfiS+P85AFcI7DPiZ6\n2+DZGk/bZ5gQxnooZVnSeo/vGhyBZZnh8cyKRzN1nMi7Gh1T84NedFNok/ymzzcF0/ldFzVZwNNr\naeVfwv26YKAujKkVAj0XauVFOwPkN22EpIqJNiQkETiFfIkhpeEpmmlJ/ooS1rbtyKi2Xq+5uro6\nicZmWcbl5SUQ8fZCSf3111+T5zkff/wxwBuQKU1eIm0X40fgWLI/HOE0ev04p6idGy+PXR66Bxl7\n1lqapuH+/n40YiUHQqMutCIjRRjPJcJ/KHIOsvSnlNRgiNDzfDRc5Znod1Mb/noOlOPlvPrvueum\nn6cgX5pIRf/VLH8pJCqFwsn7v1gsTohQxEARkheZO8Vov7i4GMfxbrc7yW3S85C03Xs/onPKshxz\nfORe0v6Y6p+HxsZjn0em5kw47U9BFcnvcBxXUv9MkxnAdG7pQ/I4NZZ3kDQ6MxUV0p+njCMz1OCR\n/J10uxhcAbXo6X2IsDfCEPmxZvhsCCYuvn3f4wmUZVyIgzl6ctMFAlT9C8Gd4uj9wLolUZyYwYNX\nSbXGx0HVV57MBIxz9M7Tdw2+63EE8qwcLWzywctrPK1rcaEneKADM7TJOYfzHd71ON/jcHgcfd+y\nvt/Qts0Iw4usRjvKPMf3PXkeozxShbzrOn71q1/xySefxHycPOdQV5F5ab+L92ENXedo++g9fnX3\nGmstdXss/FcUBdaaIT9niHAkRnEUoWP+gYl0gyfmbTU1rYUqtwTfs+8ysszy4sULZkWJc4HFbM7V\n1QVt11MYMPgPKrLzXfY/t/BMRXe0MpAaGufmpCmSDD35p5TTgkHXi4A2JlLFRj6nOTgplFPaIMaK\nXEcMABFZzLWnVDz60gdyDq3E6PboBU/aIorIq1evqKqKxWJBURQnmHvph4uLizFS/Jvf/GZUNK6v\nr0es/m63Y7/fj95ZiNEjie5or62uvK4Nv3MGj+6vx66YfBfRz+1wOJy8A1LPRDsN02ihZu17rPK2\n+eB9MHCm5p3U4SEOEm28a+YzHZmV86WGqrzrU89VG73a+JVt+tpyLu100G1Ki46m15qKxi4WixMS\nFKkDBcd6W9vtlizLuLm5GVkFpeixrnej2SrF0SP3LuNa5yulTrAp5T+N9jz29+JdRPeNXiN1Pqf0\nibCMwmntoe/ST4/G2DkHETgXwZn6O7XfOcXDKOrpVKyKIMVzPNTuaCxF9TAAYYiQ5JTzGdijgiHU\nsSbLokGhWEVk4dDJdYEjY4hzjsyEgcUt5gx1XYfvBo+sGbwhfYsLnix4Qu/oB8Yy5z3knryItVV8\n6Okd5CHSYHddhyUqAobEo9t1Q9TK4AhUfcv69S2+b5nP5yNXPV4qlWcjW48w9rRtyzfffBPzbmYz\nPIZDXVNVDfvdgV1TUVWR0aTpO7AZh0Mf06WyCFkbPcs2J8sdzp2GPo/P8geQGDta3ZNfhzyzHmpP\n6x3Bedr6gFsG5uWM29tbcpuNUMBZM2OWZ/hQYAj0/nFOxueiOVP7aHmX/XWkVy/46fdzc9LblGi9\nj2asEU+keL4k2RiO9R9koZdIjq6Xos+deiLt8O6LISBtl+iI3Jt4TLVSI9t1UrNQOKfJx03TjEQL\n8h3g/v5+JB8QiInuu6IouLq6Yr1ej4nyEqGR4qpCVb3dbrm/vx8hcmLkCHmBbq/uu1Rh+SEoIu8q\nOqonhiVE+I9m3Eu98lqBfczyvrb/3BymWc00WYFEhfX8Ig4VrSelzzG93pTinjpc9LVlu76O/k1/\n1saOnk+nIkPaINHwKH1PGiIl7/jhcDgx4oS4QAgKZH8Z5+Lk0ZGoqb4RIyjtr/T+f0ii10dhvdMO\nN1m3dL9qA0g7yt5FHpWxc85gETnnNT0nD+03BVsDBurdN850fFGHf8EMRAABhP85WAPeYLOCLBhm\niwX5gPf0PlYOF6iFrkkh13fODbk2w3XMcK3gCcEhKm02eG59FymejQ+QDwMkxPo4eXBkxtIPMDrn\nYiQqB0JwxEt5AtCHnkO1JWszMuNPFCvf1nTDRBA9oo6uq9nvt4Tg8P6SpqlGg0eHImezGfP5nPv7\ne0II7KuK7uVLsBk2zwkG9oeafV1xqA6sd/tYkLRrCQYWqxm7fROjYXk0eNo+sFpGz0/ft8RLaeXy\n8XsTv5MkA9bACPP0LoDxUDd0PuC6FuyC7e6e+7s7FrM5eR6LfW1tgblc0QdPjiGE93ORf5s85A18\niB0qXcSnohMiesJOj4fzsI/UsEmP195NHeIXBURY0LTXcmoO0QuEnFc7UlLDTKBxwqZljBkNCmmT\nOCyEljWlvAZGqEh6fonkVFU1nk8zoomyInW0tDISQhihsXJ/Ak3ThUA3mw273Y7NZjNC5KQNwsgm\nRpjue527cE5+UPPJhKTRQIjj7tWrV2RZxnK5ZD6fn0QI5Jk9yR9GUvTKlKTPS8OINAxMUzlL3az0\nnHo+TK+rFVQ9t51rnzHmJKcv/V0X6NTnlDlMz4/A6IiR+5CCqXJ9YaqUiK8uRjyfz0ejKG2L3JvM\nveIo0v2q9dE0T/iHKOccehId08VihelOO8Z01AyYZNQ9J4/K2NGD5RweHsD7Uw+q9gbqFyz1OGTI\n54BPlBn565XSczSApNZCNGYCBpzHYui9w2PIrcVkFrBYm5GVM4rcjBzthc1OKkyHiHg78bz2fU/e\n9xgT8GbwyLh+uMcBdtE7Ou+wLtAcKsKQz2JKmM9m9E1NV1V4HL53GB8jQJHwYAV5jjMWk2dkWUER\nHG1Vcf/t13Hf5SoqFs4PELLoje1cz3w+xxFoqy2+rTn0LaFryWcl9+LdHp5VUcywRT7+q/Z7CDGf\naLu/p65rbFbQ+J7ffP0th7bhUHV4E2F0ngB5znI5Y9c0Y46OzIEx2XBG2+qX4UjSAD8cEJsZ7nt8\nY4ZoZAxQDh/6juAdYbvmVVtzfXmF94G6bvn0k89xfVRoc2MpLTj3OKNj54wdOE3Sf0iBndp+7rf0\n3OeurdunFQKZD6YKcwLHWlYDPEMUEW1spUaO9nqawTEixoWcW3t/peK3nEcMD2CsXyEQNcHD6/OJ\nYrLf78caNjrxV+Y2DSOTyI4YLLvdbsjHq07uT84v963PK9cUvP56vT6J/Ghojhg1+jnJMxXnjjY4\n9bP6IYruAz32RUmUaNpqteLu7o7FYsFisRgVR0kgfzJ4/jAypXCnkjpftINE17YSEUNHFM40Aqwj\nxwJ51eeFU2hZen1trEzdjxg7U22T6+rosjbCRzj/oDtqJVpyy6TdOnItMHvJT57K+5V7nDJodP7T\n95Fk/xjl3Byp9WhNgKH31/mewBvGzneZfx+NsTMl56I96fcpQyf9POWVSI8XDfHNDn6L8oJaHFAK\nxfDSjUZOZjHW4Nru6IkNHgIRZtS2hKZmCFfQuSEZ2cbq6aYfEon7Hu8c7b4ieE+GwfU9BBeZt/YH\nZvgIY/IxCtX2Hk8gFAXOGGxfYLJIWhC6jup+A85TuABFcTIB9N7T9h1IMbChGvpisWC/32Ery3y1\npG0du/2BsizJsgMmL0bP33a7jQULdgbpAAAgAElEQVRO3WDE9Z6+q8BEGJ3k4LQecuMhy+K9ZQVZ\nZkYD1/u40Gol6/g8j5/f8tg+ALEP3p8ZcsmwMYeM4COZRd9DGQur5dmMuq7Z7/dYm4+QJptZrPlw\nO+9d5pVzMgVJkN80nO27wqFkQUgVCHkH5VxyflmsNXRFPGfiYJHtOuIS381jDSoxdLQSm+d5ZFKE\nk2iI9rxpQ0KiLofDgfV6fUIGICLtE9Y4bezI/Hg4HEa2OTleIknC3KNr6IgCI3XM0sRrMR7FoJH7\n1vCX1LE25TB7VxjFhyhTCisck8z3+z3b7ZbNZjMyAwInyuaTfP/ykDKtofGyr3a0pnk36dwnCr02\nkrSTREeA4PisU0VWz5XaQy/ziB5bKYw0JRSSuUNDzXReonY0yTwoY9HayOaq5yXZX0eKpZ16Lknz\nls5FvM5FvUTSvJ4PSbQOfc7onlpf02KjMs7kuUkE6F3X0Udj7Ey9JOn2KThJuv3cZzFK5LgpQ2nS\nAHprmw2YGEWwANaMhUuNtfhB+RgpqJuG3NiTSSWEgPNDUaq2HV+wtm2pm5osM2QY+gB91+HaltA7\n2qaJlNnG0vqWDIN3PdVhhwNyjtfwBHyRU9c1XQgU5ZxiVuK8pzlUdIea4Bx7D31RwqC49d5HFriu\nxQTIihhdWt+9wmYv4j3VHc9MzOfZbDYYYyJcryhHeFuW59y/XkfFpciZeWj2O3xwXF5e0vSOxvXQ\ng3NgbWC+WA0TXBD7D4PBOY9znq7rsfbDZvt5UKYxl6MMFaEw+GMtHgJ0HW1V0y869vs9mb0jhMgG\nVtctvjAQnjyyT/IkT/IkT/IkT/L+y6MxdrTWFnNU5LP2sA2Y9iAei3icGWEJx7MZc/yHFAqdsF20\nYeMTg2rcPfSYIW/G4qPX3HpsiHk7FkNwLta/MYbMGAwZoSyY5TmlhH57R1HOsFKU1HsY7inWwXF4\nIwm1+2iEdB0315c0m5rF1QXtbsdhv6drKpqqjseagM0NYbGgqWp26zueXVxhi4zdZksx4HN9X5E1\nPdYD2Z58teTgI9POZrumqiqeP39O6Y/wMKn5YYzBtZD7mCvU2IL7OnpBut5xePV69LZUVcVqtWI2\n63l5ezt6hXoCfVOzWq3I5jlLs6Lzjma74/piBTaQ1y112+N6T1XvIhQi03ChwMAtgckYCmCewoP8\naBBrz+IH5p0NCsY0/J3KPAjen9z6oqox3kG1ZrfxtLS40uOqQHfXcbGacTFbkPE4PVAaHpZ6peFN\nbxK86Zk652zR3sPU0aI932mSfXoe7bDRURndfg21Eq+XbNd5cRJ50HABDVsTJq22bUcWtaIoRviY\nbJPrl2WJtZb7+/uxjbPZbIyCaLpn+TufzwkhcH9/z/39/Qk1KxzhMbqeh2yXxOCyLEeInCYUgMiY\nJPctia4QPcqSJyj5Rsvl8iRK5b0f2Z2kLXqMyHMS2E4KD/qhwtjO3bfuO4nAHQ4H7u7uTqBPMl6X\ny+Ufp8E/ENHzlsi5nLOpyImMaX2MRGv17zJPAGMOjESNsywbiYlgGsql3zN5X3VkKY0Oa8hTGh2R\ndkmNr/T+x3V/eNclX0dHv7MsGwlNdM6OhljJeQWuC8fcs3NwNU1coNs89SxSmOyHIHodeEgegi4K\nOkBHCPUxHyRBwdT3qYGNegkeisa8Db6WXndq29R+EtEJwZ9VCUMIGHssFHqiSD2wfgq2fb8/0KgE\n3BAC3377LXVds73f0DWRcQgfIW4hD+z3e9av78AH6BxFnuO6ljbLMHUL7FmuLiOcLQT21YGewG63\no24aqrrm9d39iHFtXc+LFy9iQVSAtoFg2FdxgQshXnuz2dD3/QhBEXanxWKBMWZUOrIip6oq/H7H\ncrkkyzL2dTXe42I2B5tj84aqaajaQN9XlHPhYY9GsKDXXACJ141Rtid5UIKJcLa6rjE2owsBYzJC\nHyiwEd4WzImB+ZjkHDRDJIWcyfaUSeccXHZqsUqhT+lkrY9/28Q9dX4xFgQ6lBpL+rzyWcPEhHJV\nFvr1ej0aM/v9flz4ZZ+qqkZjoyzLsY6FKCk6uRkYFaHXr1/z+vVr6roeGdGEUOHi4mLM2amqaiQR\nqOuapmkoy3L8TfKT5DnJ73Lv0j9N04x5IWLciQIm1xbaWcl30lCbFPYm/fckb0raLwIVqqqK9Xp9\nYjjCkVjjSb5f0fPW2+aSKUX03PhOz5U6fdoBuu69Z7FYnDBCijEi5y7LcoSNybkE2aKdOWkOkcBS\nJX9Q4Ey6zUKUJPBc4MSokWuFEE7q7IghLoaVdsboe5V26flnak3RxpIcP2WIpn36ocm7Gm9TY1XW\nSp23rtdigUd/cMbOQzJlkOjPqUfuoX3PnTP97eSYEJnMIhTOYII5ZsEDLgTyNx64JLIpq9YOXguv\nzmeOKroLsZDo/nDgcDjExXzwxjrnWG923L16SbWPNQ+C66I13Fh642nrhq9++2suliv6q0syY8mN\nxQ84+aIo2NcNreuxJme+XLCvKzabDdtDRVXVbA8VWZEzn8/p+57l6oL9fj/281ihuHd0uz2LxYLd\noWK3240e2MjW5Gm3u1ER6bqOxeUFi8ViGNA5juiN7Z3H+Z6iKGjc0cNS5jEo0TQd1kJRiPdVXp5A\n8KdJ6U/KysPiQgDX0VV7jIsFYcHiO09pc+6v78H1GPvh9WP6zmuj5CG2tofmkXQ/Lfr8v8+41Aux\njvKIpPOaJOwDbLdb6rrm4uICYwxVVfHVV19xe3s7tlHXpei6jtvb23Exv7y8HCuVCzxVe20j1XxB\n0zTc3d2NtW2EqEDYjsSJo6mLIRpjYvBIW7XXdz6f0zTNycIoSfA6Z08TEIgSJvlI4rWVHB5tqGnP\nrlbY0n59klORyA6c1iURSSOWT/L9yVSfpnNbOhelxCxTY1sn3+vIiLy7Mk8sl8uTOUZyBMVQkMit\nzgvUEVRpj2bu004bHaGW+5F8QzlPOl+LwVXX9cjuqIsL67pQEkmQbdIGff30vFqp1/uk0eE0X+pc\nX39I8l3vL43Y9X0/Rts0OYU883dlZHs0xs6U5wimk5sM5mRbmtw2BUuZss615+NcKDJGYgImeGLa\nt8cHjwkxB8JgMEbq4kRxJjK/2chcMJ5n9MgENxS7H9rGUVFvekfTO8gzVsUFq9WK+/2BV9++5Mvf\n/jp6XH1gVmT0bZxkfOipfM9hv+fly5fMyxl//qMfkWGoDjvKvCCE2AfzxZK2jwnLzjnmFyt2u12M\nuJwkES8wecaud6NXRyabEAL7YYAWg3JSec/rly+jcjNAZkIIOBWd2q1fk2dlpILdbYeB7qiamjwr\nCdZwsViO/XWoGpzzzC8WJ6HmPI8TlSfQNr2akNK6O99pCP4gpKcnOGC/w7QdoW1i0db9gWa7JTjH\nzc0Nz549+1M39XcSvQil7/c5D5TMH7IQpjA0bWzoeWTq/FPzyEORA+3p1N+n2qqVhZT5RxsDafKu\neFpDCKzXa7755puRgECUE9m/rmtevnx50idN04w1ErQXFjgxJtbr9QgBkQVKIi+ynygd+nqiNAnk\nThs76Xyuzz2bzUbFShwqcDSCyrI8qbUhSlUKEQwhnFXcHzJuf8iSevOrqpogjHlyPv2+ohX6hwzH\nt41TPVedcxDr+UsbM/J+CXwtz/OR9ASOhEFSakL0BQ2J04aQzFXp3CdGlRAR6Ihz2gepx1/OKXPD\nFExZHzMVgZyK4si8krJYagiebptEJp7IOd4uAinXkbF0jU2dUw/JozV24PwLHB7Y9vtc75wYomJt\nNGzKx8/BMlD/JlEaAtkZL8r4EnK0cAVa5glkRR6LkuYFxbzkfr3mbnPPq9drijwyixibge1p6pbN\nZoMrM17frdkfanwI3G234B1NVY+YW2Ms+76jqppxIiuHGhsaI9+2LbZpWC6XzOoYUt7XDfu6GWEs\n6/tN9LK6yCAVMBE91zt8aKmbdoSwyORqbUbvHWbw9Fg7RGq8oQ89wRryYjYaVV3ryLJAPgz2Y7hz\nwM4STqq5O5dy/H94YePfV4KQGvQdwQd852i7QN+2zJ1jff86jsVH2nXvEoGR/d5mmKQL1u8632jH\nyzlj51xk6Vyk4Vz+ULpdlJP5fM5ut+P29pbXr1+fsA2FECI8dois3N7ejtsl4tMOxCkaUgcxMiMe\nucPhcOINhhhZWiwWI0nL4XA4Uap2ux37/X5UOsQgEsVZ03FLe6c8faKUCIwFGOsBiZGkYTci8rzl\n+lpBf1LWHxYxeESxlai/bJPtT/K7y7tGxtIxe86pq0V72NNt8lyBMadPoiX7/X6EMMrxAn0XpVUb\nDvpdFoNEK7HSZl3vC04ps+W8ei7V2/X963pk+j5TIwqONXi08Z7WeUmNJP1dGzbye+pcf3KYnIru\nizSvSvetjLlzTspUHo2xI5IqK+eiMlMGxDmPRbrP1PFT+0ydy4WAjZV2krbYE2hbeo3JsLEBjRby\nBPJixtxkhOCYDUWXAuB8oJjP/v/23rS9bVzZFl4AJ1GD0+nbd+9zn/O3zv//eN499JC2Jc4A3g9g\ngUUYlOUknR0ptbrz2JZIEBxQrGFVFaq8QL3fA86XTBwmi6bvYFDiuWkxjBOyssBLc/GV28YJxZz4\nm2UZJuNwubShn0Xp/GIenIGzai4w0PlFXFb4/flPaK1xmYslZFmGqqrw0vqcnVoBmc1gFWC1QjsO\nyJ03cLTRqGYan7MWlfUP9DCNGMdpFb7u+x7Vvg6JakopXJoOudIhfE7KStf14ZoVRcE8jLc3oPph\nMT+4yjkgVL+zMHPS+/OnP+ZCHfcpnN9a34SULOEGT+zFe0supMaPt78mD/hLNKap8SjVrS9Nop7R\neuWlVmO+fd/3IdGc/lFE5Pfffw85ObQGlVr63Tw/e6cHFQYgpYErQoCP4OS5rwbJDY+29RRYargM\nIESSgIXPv9vtVrQXui5UIp+uIS/ekOf5Kt/IOfcq2ZgoOBSt4lEuwdugZ5YU4ljplGv5dcHlUgrX\nIth8m/hvkk/c8OB5K7SmyClCkV5gKULBy7pz8DLVFHnmNDMeleGGFV/XFDmi3B8aC1iMKB69STF+\nUhiD43WRj9yQIscrL1jAG5bGUYjUPbhFZm85rX4E8B5nZKgCS485cqC8hbsxdpR1UGp+UMLfM4WM\nfQYgeJ3jB4obPCnESs21cG68vXOOFRbQcDMVzdmFxhYfNeXFdc75EmJzXxm7Tv9BsauQWa/AF7nv\nMaPKErrIcTieUJQ54AAzGDRdi+eXZyidYzIOk3EwcBgdMFpPibMzLW7nqJP4gOfzC9ree09dMQui\nvPC9eyaD3k7QyDEYi+fffoeeufxd542guq5hHDBNBtlksMuLuZ+Lxrnp4dyIPAfqegdtbKjsZRxQ\n5AWGycA4398HAIqixOQshmFC1/8RmtOdTicf1oYJign125imCZNd+mjECqngOrQCsrlZrnUO1o4Y\n+w5oztB5luwqfQ/giu01cDkRywP+ouTgFdFSMiY1Jv/9WmSHvk9FFzjtg1PVUuMTz5nPlRSksizx\n4cOH1fGpQAEVFSHvPL3sx3EMVDZOJ6BIMPW4ISWAKqNxvj2NSefFq7XRuub3zbk1rYycH2QMcRob\ngOCBpjnQ2GTYUXEEmkNckIFf0/fKj/cqNY8G/szySnlER+QFIwS3IRXhjRX8GNeiu6lo9TWDKY4O\n8cjqMAxoW+/4pHtL8iOOTvNjxXKBr1N+jpz6xilrFE2OCx3Q8bkOACAcj47FDScemaZnls6RjssN\nl1UzeLfOPaK58v4w8dy/FI9sCNH14bRr/p7h1MS3cDfGDk+Oi0OWwFoRAdbeiC1+d2qf+HixAZQM\n+SqirtGDa+f/5xf0TG7zjS8Zfx+L58s558tL05yVglLzvJU39Kp9DTexkpCzEVVWFX7529+x2+38\nQh1G/PnpD6iiRF5UvtzrvPbabkBV1Wi7AZMZ0fUd9rsaTiuMxqEdJxilYeEwmgmDmbCrCjTnM/Ks\nxGQcrFMoiwLWOYzWwJkJu10NC6/cjLPQ6nsD57zSQ4KiKBSGwaEbgWnqgteXkp+plCMpUDBAVheo\n6xrDMKGflZK6KlGUGbK8gpuF4MLHncvYwtNvUsnFb2EtPGIF9OZh7g8zu08ZwMEAUL5X02jhsh59\n1yIrCzStKCkCgUAgEAi+f9yNsbOFt6Ixqd+3IjxbxhA/TjwOMEeSrJuTd2bTRgHKzd3NlYLTDplR\ncNoufYD0NufbwvlMHxY6BgCnWZIgfDQrKytULDGwsS/IqgrQClYBZb1DN3RQau4UbC3Orfes5VmJ\notwhL5ZeGdZawCkMw4ii3mEcDLTKlwIAZQGdZ9B5BmO8l4KXkh3HESor4NSEfrTIRoM8V3AqQzfM\nSYcKUBkwGoemG3z52X2JYRhwOByw2+19yeuuAzIdEhj3+32orKS1XoWVySOTZd5gavsO1rz2dH8u\nlsflR/PW+hLqbuiBpkHH+M73hmuFTTi2aGyxHOBj3pJ0+laEeYtmR885p2IQ4kpBWxQ4mmPsyQR8\nhKYsS/z888+o6zqsZ4qc0H7k1eS9ajilI8uyUGyAzmMYhpWXl3t4eS5M3/fBI8oTiek77pnl58/z\nB/I8D1FH5xyOxyOapglRX05/4R256fpweg6/V5QY/XXkyPZ9f0TwZ5rn7MQecMFt+NxnMJZP9Bxy\n6tu1yBCthbgICb2LSf5wLzzgZQSv6Ejj8XcIRXBpnVKUlbYleikdh0eBeGSI5AmnR3KnN88ppPG5\nbODXhM6NWCucbkfPcSxD4sIEKUrglt55DW9FiB85ukPYorzeKjvvRmOJIzuEVISGIxW54T+3vkuN\nuWlQwUdl/EQdLHyujYIFrPW5N9YBSnsDRiloOFgbJcy7JRkZQChKQN87AIoJDGcsDAxOH3/G/nCA\ns9b3zMhzHE5PeLmc0Q0jqsMB7uUZsA7H/QHD2ANYQqtFVSGvfLWzvc6hVYa+6PH8/By495T7kmUZ\nisrz5320ZcSnT59WdBifMAhU1VIKksq8FgWFggFrlwRjpRQuL83M7XcoCoVffvkFwzCgaTrfz2Ov\nPZ3GTCiy3Je1077wAXH9vVK4lOBtmwbs0q7u+1vvjOX+ryt3WbteYA+lqNhZmVRuzhdzs6ljATcB\nXQdrHc7D/eY/pZL9v4TiyKkRqSo8/CV4y/FimhofIxVpvkaLiJ0lBD5PooHsdjt8/PgR+/0ev//+\nezincRxDIYGu67Df71d8fWDpnRGfJxkPlDtDtFKiIvBKcNSbi4OoKXQsUmx4tN45l7z2VHzhdDqF\n3hyxgk2OEhqf5xUQzY3yB1KV2LYojTFiKiG/dvH75JEQK2F0zlROVqpSLUhVVouvz5cY21v7xpXE\nrs0tlR/I9TIyajiVjO4zFUKKx0yVyudrjdY4OTHouzgnh6hycXUuTqOk43GHDaf/pubGG6by8fg1\niPOH4vXOx4+v8y1GSkqGp87xkQyelNOOG6nvdWLfjbFD2PJ8prZJfR5HbrYobvE+W2MAgLN2yasJ\nN8Ln6KhgNHkDwP/z5bGdWydsKnYM//n6AVdKwekcTmlAWTgLQCsUc6KWGXocDgc/xjTidPqA5+cz\nrJ1wqPeA9Qu+siWAJRGwLivkWY5cabSmR13XYYE2UxOEFAkxrTXqaof9roZ98oLifD7DWhuiLt5z\nQ8qZhbXTrJQ4ZBlQFCQw/XdAERKUi6LA09MTdkWJQmcYBq+AVPN5qnGAcRZmmqmG7AXqBdJ8JUMU\nzK2MneX6br9o1wbx61K0/Dl7KEWFjB14o93D56P587SAmWD7YWuE7xq0bmPlOHaApBwd15wm9DOu\nFJPClhOFKxEpYwfAyjPKj7ll7MSf0/nzcyfPLPXIoTLzAEIVpU+fPgFAyNchRYE6hFO0iDycdDwy\njHhSMT/33W6H4/EYOPpUGIXAx6eXXWxwcEWDnDKAV7J8lNiXtybvLDdOuRJE15IrNRQxiqNpqWud\nQuwpTjnlSDF8JDmy5Qzi5059Tx4RX2rEbe2/9fk1ebP13KaipHybVFSCG0Wx3OLFQnguBVdOySCh\nimu0H8kFkiM8OkNV2MiZQlGdOFeDIrC03rkRRfuk1hrJz5Qjgow00i1SxhSwrgi3ZezEDZ/5tePX\n6S1cG+ORkNKvUs6lW8//boydeIFeezC49+HWcd/a5vp4Xhl0zr02epzyJamd4zwoOOUjM6tR5mIE\nizd2XdddKeX3mylyFr4qls4yOKUwKg09J32OWqOsKuzqGn3f48MxR11WGKdhTrrrgrF3PB7Dy9w5\nhSLLMdU1qrKEe7bYVcVMUfGLXOm5vLVS+HA6wU4TYN2ymJVDPntPyCtCCctFsZSr5YJzmiacTk9e\naDtg7EZUeYWqqnE8Ory8vPjI0K5CuauCp7jpOox2aTI2jhZZNitEmiuoa4PnPYiVX63XC+5RFBQA\nc5n0OUtJxS/JCZg0fCOe+zV2gNdCM1YU+JpPRXS3HCI8IpxSImLnSmpuKS9enEPIca2iVey1jI0l\nPi96SU/TFJwmSvnKak9PT4Em0nVdSB7uui5ES6hpcN/3wWDY7/coyxLn8zlEWrh3lxQTiu788ccf\nq+RhoqWQE4VXPaLz4U3mKBpDcyej9nQ6Ic9ztG27ivxQtTWKQFEhBj4ev0afY/Dw/WNjJ352Hh1c\nwQQeV1H7GnTHv+qYn3PNY2MqjpCSnIlLNQNYlR3nhUBo3ZIjg/ajhH9epp5HWqhoAHdSUJ4vHaco\niiAHyAFDx+dUVj53YCk1zZ9Pkk/cQR5Hh7mhRmPSz5SDgx/7FsTvlx8JW+f8Hj0fuENj5y2898Xx\nHu9cKjI0j0J53aHfDrhQdw4KGiOAQimEdZbweDm1pp6kvGLO+QpZzvl+J5nSIbphlTdGqCrax48f\nfUK/1jP3tEXbdzifn4OHIvDkhxGnw3EWHH78gzmgLEscj8c5/FuE8s9d14UeN1VVoWnaUH3pj8sF\nu7LE8XDwVBStsSuH4BmmOvp0bGMMcpWj2vtGgF3XhRK41JV5oE7MKoNTQD9T45AXsxCd4BzQ94O3\nK7WPyvj79ZrK9tatX4yctcHzyAqK9uTL8BeUBVbXbb6O5tu/zL8GUuspZfR8jsdtC2T4pKLIt8yX\nKw4E7hXd8pzz/eOXb2xYxZ5IGp+UBsrlAXw56OfnZwC+Whv3aJJBxI0qkjO73Q517cvHEyWF02Op\nvHTI1QNCgZHj8RgobcMwrCJdXMHinlcae5qmEJ0mLy2/PqQgxX08eD+PrZfqW/Qfugb8X6wQPVJE\nhxCf0+cY6j86UrS2+PMY1xwsfI1zmRBHf2PlnNOwYqczge9D65Afj6isPLeOywiuE1BUh+d38YgP\ngKSxQd/zfB8COVd5c+HUmiZji8tXouaTM4fLyzhSzK83Xds4GpZaB9feN/F929ovte89I/Wcx4bP\ne+TH3Rg7hFSEZ+tBicNf8TgpL23qu9QDljyuo9+Vz+PBfDO0byIKKFi2L2eqphTolCLCt3fOQasM\ngBdC5N1Qud+/Ph4C9aycPa7n8wuyc+YrqO3r4CXJdQblgKenw+wJ8YrGfvQejWO9BzI9c+z3wRNT\nFAXKWdnwfTZK34eHFSwAlm7llITMe+hwbw4lM9O8h2EIBpTWGoOZMJoJTvlru9/v0Y7TnLMDZBlA\nz7/OXbgP/N7RP2NuFwzXvDMPiZkq6X+Hf7YdkGkNq/Wcx3N/2HrRxMpEvO7e41FLGcR0DG70pNZ1\nPM84MrMV8eH7cUWGc5z5fGIZExtD5HUtigKHwwH/9V//Fdbi+Xx+VWCAlIxp7tlFkSGaY9/3wWlB\neTqAd5JQjx0qhECRILoXtC9FhnheDRlTWuvgteXXigwfKpvNDR0ybngvHn6teZLyVqTuLeUidljx\n+dG9SUXy7h3x+aTO79HO+WsiVvS2Et5T+6XyfVIyYkum0L6xPvJWNI4MjlT0kzsleASE9lNKhb4p\n3HCh/ekzKkDCDS8+JyomENPc4mPy65PKi+JNlYnKthVd4PMjI41vy/PNU9cs9dk1o5LPmf5+RKfB\ntfci4T100bsxdlIveWCtWKQ8ZimjKGVUAIBm1od1RBtZRwEUVMhfCD8N9fxZhAdFXhQMjFNwRsE6\nBWO9B0Mr5VO/VwqIhXUTJmdCM1GlNaC8ym6cg5rm83Y+x8dNBhMMFBTGiSqT5dBw+Pn0fzDVvglX\nf/4Tzvkkv9P+AG19HXxrRky5F1CdHfH//vYTtPLe0+a5gul9kYFdnqEofLJuoRTquoaaLKrdDjAW\nH48fUOkCl6xFVVQYe+99LQGUhz3aPIO1CMpKlmU4HA5QzvdcsNaiyGdurDUo8grINLpphNUZ2q6D\nU0Cel5imEV3bYZw8NdDYDEV5QD+cMRkXwtZmMgBeC95bXrLr5Ei9Elhx/X/+HN47vFFu/T8e0ZlZ\nmNZNsOME3Gk1tpQ3nWRDyujZQvxC4veeDJqtF9ZW8jEZ9PFYseHDX+IU0eBRxzhxM/Wijcfkn8dK\nBOXtcK8rPwfORydFgYwZpRQ+fPgQCogAWEV+SPmhvKGPHz+G3CEAIY9vt9vhdDrh5eVllSw8TVMw\ngkgZornxfj30edd1Yd+iKOBmJxBvYMhlMt2PrejNLcYOv878PRQrbD8CrnmwBQveS4OLc03eM8aW\nEk/gz/81RZzvF0dDSX5wB3LcO4WMHcA7QXgzUorwkjMkJZuzLAsFEqggCbA4FeJKcNyh45wLzpr4\n/IlqS3OmcyEQFZbTgINOmZDx8d/XojMpg26LgkfbPOq6ivX1LQfUFu5GY4mjHKnIzK0cvi0vrbXp\nz695dZ1b8nQcFNScZxNoZc5BOwenVEj+BgCnNTLlXo/tWGnUyHuulAq9fFaeWTg4u/YQKqWg8wyF\n8tGebL9HBqDQGkPdoSwUhr7HYMaw0JVSaNvW81MzjV/+9n+RlwWapsEwTNA6X0VgtNb49ddfUZU7\nZGWBn+qfseu6kEzYdC2yLJ85zIYAAB9jSURBVMPx6YS88Tz/cuf7/pAisVBadFBG+r7Hr7/9BqWU\n7y0EjckanM8XXLrWC8WiQN/3aJoOLluXoiaB9CULPw5FX3uuHlW4pBDnpN0jrhm8JEBjD2nKuImd\nJhxbRlPs8Yu/jwV6PGduXPAxubMnPp9Y6eDjxwp3SgEniom1NnhW+cu8bdsQNeGVkQAEvvvpdMLl\ncgnfcccTJaqT4ZPnOY7HYxifDCeKMvFy15wWQ3lFnOvfNA2apgljDMOAl5eXcK48Fyh1zb809yK+\nt1uKTOrvR8aPdK7fAu95Tq9RdddOvtfK5bVodAyunBMThL+XeQGD2Njh71wqaU8UeM8gqVZUNpI7\n9B3lBgFL42N+rkTFB9Z5gdRAlObBZT1Febizhztr+HapSMvWM3+rw+RaZGnr+3vENePvS3BXxs43\nOEjSoHp7v8QYYBSR+StvpyzCxGQWOtGzJRhvQIjwIIz3WiFy9B8JKKaIODdXNpmVEaUUMqVRFgWG\nvg2GDlFJ6AVP+TG0mLvO822Hmf8+Tb4AwDAMqA9eMTmfz4HvqrLZkNlVcFbhb3/7G3774/dQhpLC\nvXlWoq73UAA+/fmnj4jBRxi8MWZR7CrvfXUzrW3u1p6VFcaxg7NrjvCWx/09iCk/nyuo7g0KFq/O\nKNAzAaUsAH3Xxo5AIBAIBIIfBw9r7MSKbvx30pC5coyrhg9RzpSCc3N5XqYkuzB/Nf+bt9XO9+CJ\nDhuyeZRdzckbIuu//fhr7q1VwNy31P90gDVmjjbZ4FWhhMG+79A0F5Rl6b2uWgfeK3kOKLHPEZ82\na2GMQVnv8Ouvv4Zj+UIFTZhbpouZGuXDwf/85z/hZipY343AxyX0208jxmn0DVW1hnEWfdNADz2Q\naRhjobRGoTWsAoa2hbEW1i7cd7rPX8MISXm36N792LDAnebsbHlAyeOYorPxPJuYNhaPwccicNmR\n8qpyb2qK8sYjDBTdIVBEM85VIaSqA8XjpWhssTeXjsErpdH43GFCnlFOB+E5MdM04XK5rPp38WgV\nfc6LA1AEOMsyPD094d///vdqDufzOeT0UB4OH4OixeRxpbkFmeaWao6x0+Rmh9cbeEuOPJrTZAs/\nynneglSuyNZ3KXxO1HGr2EY8VspRyOXaW/NL5UDycQCsqKz8X2p+vDAJRWUoMkPyg8tC0m8ArCrA\n8WPHtDYqmR3LfnISE12Wl0wn2m4c9Xq3w/wKtmjQqW0eYX19DWd1Cg9r7HzmQcKxeLj1zYc1Ednh\nxo4Nc1cANJybE161gta+aIFGguLgfMNMDc7bx2oborGt/o7/wYUIjZ1GwDrPoe8HDG0XFJVxHLE7\nn1HMybwAYIzFNPkEY1pi1loY5wVGP044N5dVCLeqKmRFDjt6o8ZphX/84x+o6h2apkFelt7gMROe\nL+dAOxvthGHyoWcLB+sU+nFA1ww4PX2Yiy9kgFKw04Rh7nzME33Xt/Pzn5lrdJOvMf69gUz15YP7\nPPctmtg1cOMnRTl7i/rK5Qk961uVZmi82DiJDW/+Oy+9vDWX+LxjmgWnscTbx0n73JihogVt26Jp\nmlBMhPan9Ul8+LZtV9+TspBlGYZhCIYlPydSODi98HK5AMBKqSGqGqev0DbE+a+qKjk32u5ry5Fb\nxvmR5EiMH/ncrxkrX0qfvDbuNUOFr/trlLX3zo/Lwrh8PC8EAizUMJ4XQ7KBz5+XnubGCx+bvqec\nPqLjxnkuPBeIFzJJOW34M8sLEmwZal/yjL9nvEdbS/E7NPU9cLvz+a6MnRRvPoVbLMLkNtHF48e8\nNqaDz69xzgEKcFb5AgfM8AHmlJ2wWNeUNKuUL36AdUKycw6TAwAFZV8bRNzYiT83zucOkUJUliUG\n65UtMyzlF2nxG2Pw8vLiFdtZQGTZXAqa1bW3mPMaihxmjgBlWQadZ7i0DZquxen4wUdqrO97MUwj\nXOfLRTddB6W80Hl+fkbf934buyg0ofeF84bV+XwGZmqchYPSObLMhrnHnqMvXfhvCfNHEywcVHxt\n+4P7RWotbz0vKe9cKgF3a99YGPNjX/PiJtd4wgDhx0kp6Xzb2MCIt4n3j2UQrTMq3UwGBlcMqLIj\ngFURD95vi5ef5eCcdw7qlUHjUNW1dq72SN5eMmhIdtC1oRwhitpcLpdV8QNSckiZSnmgv2St/1VK\nq0BwK+LnNzZ4UgnuqTy/rzkf59YFCugfz8mhOfIKbVxXof3JCCEDJ5a7VD6fnE0kp2g7Hhnn1SX5\n3MjYofF4hIh0FV6k4K9CfC+/dUTn2nvyP4H3HPuujB3g9c3dSmZ66yLEhkz8GY1xTZFJUVVeb69h\nYaDnbdaUtsh6VX57Z5fEvZWCA4elRU9CsVFYFTCw1voMDGuRz8bLSEJDYfGedIsHtes69LPw8AnB\nWRhvGIYQ0UGmMfY92q5HXhYw/dwJ2/mx+/H3EHqGVjidTrMHJIO1BtPUB4Nqsgama9ENcy5OUWAY\nDbpxgIXDrqrw0vQADEYzIdNeeBVVOQuudSflrx3+FCTg7lOJ41X1rlENtjybKeU1ZazE41/zpnLD\nJabQxc4WUhL4eHEBBTomsHg/UwZO/DP+LN4WWPracDoLUUrIc2qMWVHJqEgBKRlEAQGw6k6eSuql\nZsQUlTkcDmEOAELVJTomH6OqqqAckeeXIjw0N6q0xCvKpa7jX4FvKaOuvQ9FVv51eE9pXI7PNZK5\nfPvc8f9qZZ3Gp7VK644+541+gYXuRoYKRYljg4ScIXT+nCZH58vpuySjSA8hWcFlKckFOrZznrLL\nmxqT7OM02JjS9rlr7Edem1/bkLsbY+faQ8SVF6UUoCwFU6DYf/Q3QYHGWP5OKT5h+w2FQiGD0xpm\npmBprX3ejaOXp4J1sxdDKV/1zQFG59BaAQ5QehE+k7FL1SsAzi0v8Jtydpxb8nYc4LSCmvvo6KKE\nGyccTyd0bYvJWehLhrrewziLS9PAWswcVwWnmnDtLXw+Uj+NKFQBneUwrkV7uWC328PCoe2a1XXi\nClCmC3z8+BHn8xnP5xcvHKYRZpyjMyrDYAfkZq6qUnsOflHtoPIMo5kwzdv24xB69wz50kQs9pr/\nFXhkJUXDYvPqKR+xpC3vEcn1m7jGKYVh61685W3biiSljC16fmNDK0Uvo89TUaJUFGdLiU9FjuL9\n+blwGl5ZliFiQx5ZXv2Ior5E3yMvLVc+KOpDZWO515aOS4qLcy4cB8Ccc9iHCE58LcmhQ95spVSg\nrXEDh6I8W9dL8HXwrT3R/2mk6K9/Ja69L+LvruUM/VXgERpa47wJKP0cqWE41s4cMihiqit9R+Nz\nShnfj/fN4ZEhAtdb6CfRdkkucvlGjJmqqlZVHWNH+F+Fb72O/pNRnC/F3Rg7hC0lgX8Pt1YyUspG\n2DZCygq/Fvmhz73RoQE3N/FzgHXKU8yczz+h3JuYEuOcg7Kz4QUTIkAq0NyYhzjlkcX695WyMv9u\nla9wZpVClufY7fZ+cRqDXX1AlvtGXV0/wrnFo7KUgvT0tXbo4cxccsA5TNbn86gsD57TfhxxOByC\nEBr6HrbtcTgccOla7Pd7HJz1ndIH3zBwNA5ZAQyz8kLNB8ETw6Gh50aXkzVo2xZZViTv14/yMv1P\nwNvQ9xnZeQ9Sxgpfu9yweEtuxH+/FUFOGSbckOf7UtGELXm4FbGJv08p+twQ4ttSZIX3wjidTtjt\ndrhcLiulgZQBfq5EQ+OGE0WsqIgB4Ne9L02/UOaoEAEA1HUN51yIFk3TFMYbhiFEbvhx+Plyzv2t\n7wOB4L34VnTGLcdNDE634lFawpZj5JbI9rVteYSayzLKySMDxFPos9VY3OHBozUk/8gwoe15xJzG\nJocL35aosdT/jwwiPjbNJ5XvQ3PiOUHXZC7fT7CNt+Tue6/hXRk7m8YNw5YXlX7f2if1eWq8jYkt\nikD4idVnfoGsFwnnqfNIFSmSi7GzPpaNFpJlzUyDIHFrrv4E5Y2FLIMuSxTGoLIGTukVr/3ctMgy\nL3CqqvLFBJyDUxqj8Q0/AUDpDLt9jXPXYppLP0Nr6DwH5jAzhXh9iNjPryxLjMaXr/7p549omgaT\nBaAH9OOwEkLTNGGyDnnuGxuablh1Tx8ng3GYkOlsdT34PV1fOlFaPhsPIptvfQbiZ4kMd15NLX6Z\nx8ZPrExw+cWfzzhaHb9M6QVLkQh+LIpUEO/81ugT/zw2dvgLnZQEnhuntV4ZLwRydsRK0263C0oA\nFSLg1dbIy8p575x/b60NjU3t7FzhSs3hcAgeV34eXI5wbzB9TnSY2IGWui9bhqJA8L2B1t+WfALe\nVhS3nnlugNzqZIzzg7jM4WNyueDcQjPjURQyVHgUNtX3hp87rXnStyjazOdHRg05P+K8PnIqURSa\nZA3PD3yLhbEV7RHD53343KjZ3Rg7b72Q+HbAbUnI1wyZd3n0mKeCqHDc2HGkKFgHqIWiMmpAOYtc\nLTzT1TGVfWXwKLc0K6Xz5ZEdLkwMRZWshXUKVgMqL5BnGtM4IC93OMyejHEcfc+clzMAINOeZ49s\nFlCq9VXbxgFQClmWh6prpQPGmXpSliWc8gpGkZcznzUPfP3dbodubjqqlIJxCn3fIS8L5G7hzPLz\nIX4udTFumgbTZIPw42Founfr5+Ez7qlAIBAIBAKB4O5xN8YO4ZqFDLyfE7xFJ7klhBa2iSges721\nGB9kgJC3QXlvgNEaSlk47ZBhnYO0HMhSN1IACCWqHRZjyiiKBL2mnNjZ4IHWUC6HUwbKKegsR1E4\nuNnrQbz7D08fQyi3qioYGBgzR3asQTUOsM5Bz7z5w+EErVuUmLm3CqgPe/z222/QOgd0FjzjWuuQ\n3FeWJYbRvPKgeENmCoaMBSUeO+z3e1SnE/I8x/l8hjHp/Jxbno9b7vGPCg3gdar4vPbm3x/NF/XW\nc0KRga3oL7Dm53/Js8UNfn58ikJwmgSnoNC/rflxGRV/xhNzeWSHyxJjTPCa8k7neZ7jcDigKIpQ\nGYmoZyRHlFKhihpVPgMQ8m8o0k2RJKLJXS6X4Hklpwy/1pQDRP/2+33YhnP+eYlpKlGttcblcgnR\npFSBhLei/lvfCdJ4hGuVYph8T9iizKWot/R7jPj8bsk9iiPEqflw+cNlCNeneCEC+ptHq6j5eUy/\nI7lIERwe7aE+XRQBJh2D9ueRHzomgZe15rIN8DKGeovF1Ds6zzjSL/g6eK8suStjJ7VAtyI79B3t\nk4oGbXn9t/ZLbQv4njCKR1p8YGdtdFgLBwc1R3eA9SJ2yoJqFKxoIBaAcqFgQaCs8fFZi3sycPix\n4Rx0kXtjxABKASrPoJ2DgoPVGpn2BQR++mmaozF+gVvl+bROAZPzdDfrAJVpOKVQ1zX6cUSmlK/K\nNvNi6/oQlJeq8sKBFKWma/FyblDXNfb7PSZrAtee+mBorTGNBoYJvLZtfVGCosBPP/2MT58+rRoW\npu6TQBAjpfBfo8dyIyBFBYn3v2YY8fFiOcRpciv5wowbTmmL5xDvByyGV/w5/3slK974Ll5rXJHg\nVdIOhwMAhN4VgG/uWVVVoOLReLwRIBl0pGyUZYm2bQPdjYwbGoPK1u/3e7Rtu1Ki6DqT8sQpeYDP\nO3p6egrNUFP3if8UCIDv+3lIVWYkkNyI+9TEazq1/xY1jh+TwHNr+LHpM75G6TOaF9Fc+Rwoh4YX\nHeDV2PjnsaFD207TFOQHybNUtTeiq9GxY9owwKrYwssQasBOx+bX49r9uHa9v0e8RdP7VvjcY92P\nsaMoKqJCHxh/8UnVX6qqZVQpigwWw6ob+S/moRSW+8cVIDdbBDzCol4dn5CpJWHfOQcqnuacA6yD\ncwbOUXGCZUhjLQyrMqf0rFBAQ63KsdGhXajG5o2n5QWu7GJwKQCYRuTGeKOlKKB90gwMHCYzYrAT\nkAEqWx6BDMBPhwO0zlFknnpmnefRuqJEvttjf/zoG5JOEw6HE3799V8o8wpZlqGuaxgzomkaPFVP\ncGpuxmUmjNOETHtBsj89hWNO04T8MDcfVetuyKM1MNOIoR1gMQeocq8AQSns9gV++vmE9jIsfXlm\nfv5bL6R7EC7At59n8GdtHPY+rto2rjlMrkWJU9uknCOEVIW0eA4phw33LvLttxrn8n14Eu/W3FPG\nETcuYuOGG1pkaPDz40YXeTb5+ZBSQ9EaiiJzhasoCvR9H9ZuURShgEHf96jrenWevHAK5+MDCF5W\nmhPRc+kfbQMg5BJprXE8HtG27SonUOSI4HNwzTD4q4/Lo5wcKcMj/jt2tHBjn+TVNcci3z6WVeR0\n4eWheWloKh1PzkueN8OZIWSkUAVGGpvG5efAozO0rikyxJ1RJA/oOFxO8lwepVSQhzQ3kmlUPY5k\nFDfmBN8H7vqOXHuRx79f897GlJG3PL4pRSWlBL13/mBjO9bHZNnGhualFNEJXhHnzT49N/x0c9EB\nAP4zM4ZxuqHD73/87ptt1fUq2dh7LPQrhaWcCxXozPe46fseWmc4HA7opzEIA1IgqtLi3FxgrcX+\ndETX97i0TZh7VVVByOn5nPpxWBShTMNO4yzEfDW4LAPUTDdxQBAyXNDy+xKXkn3v/RE8Hm5VXlOR\nXvp+SyZsYcvw4ftwhYLP41bQ9hQtTsm2lNwiBYAiHrF8IzoJKf7U14YoYp5OalDXNXa73SoCQ+dL\nc+IUD6KSdV0XDCWil9R1HQyc2FAhLyovfU3lqum8SO4BCMUPSBmKPb5kIDrngreWjkXXIr43gh8b\n71nv3xLXIsq3zIn2j40fXgyF45rjJ96eaOncUUHrjWhhbdvicrmg67qwzun4VOCEGx20znmvGzou\nj67E0WLumKG5c2NIKRV0Il5djRdEoGPxym51XYciK9xZdO1ZuSe5ci/z3MLdGDsU6HiVM8AET/hM\nbRsy/HbdSmdDtM/r79cUEIX0g8GPn1Sg2AKhym1rZcWXsCaDwTqmpNh5Hv7k4axvEOqpaxYKC5+1\n6S7492+/oigKlLsdfvnll1CpqC5rOLcIE52tPallpYP3dBwNdoc9TtbgfD6jLEscDjXatkW9y1Hu\nKly61isaWYbJmqAskYelqioUc3RpmMZQaSkriyB4y7JA5tZVldTs/fHG0lJ+mgv7rXsgEMSIFZhr\nDpNrMiH+PB4rldeTMnKuPadb35HnlSs28cuW0yvoe6JgpKJK5EVt2zYYPcaYUBr606dPMMbgeDzi\nl19+Cb2v+PlStThSSii/B1hoILvdzjtO+j5UVwMQFBqSZWVZ4nK5BIOEzpUKnpzP57AP5QfS9aUC\nJ/Q9yVFeBS72XseRrlvug+CxsSUDvgd8rqHFn/kt42brs9Tnscyh9U9OTl5xkaiuZVmG0vWXy2Vx\n1jIqGFVm5HTYeM2S84JkRN/3QXaR84XyDwGEpseUt0xjpq4Nr0oJLPlFvH8PfU7bxxGna9dR8Nfh\nbowdwlvRlreMlZSxgSuf3TirlZJi+RjuNR8+RG/i83FLpAZ47RFQyiGDDttYt6axWeujJFopuDks\n3DQNxn4AnPeIGDPi5XLG//7v/6KqStTHQxAgx+MRVV7BGAcz+oVflIvS4uejgqDSOselu4SIDnlX\nqqoCVIHD6YjDMCtK0wSdZ/jHP/4BXte+rmsURYG6rvF8fvEeXmtgKVfHWShoFIWPApnJQSkHrdTS\nEHCWRSQUeanc1V0S5eSHxy3R2lv2exWVxZr2EW8fU0u2uO9bykpY8+xFGx879QKNc3v4/Oj7uEN4\nHP1o2xbn8xlN0wR58+effwIA/vWvf2GapkAD+/DhA37++edwvHEcg7GTUiCo4Sf3lo7jiNPpBMAr\nQlnmI8ht2wZlghQday2apgkR5bIsV/k3vAEgj9TQvqSI8UamXNmjz+N7L/ix8Z9WVmM6WZxPGH93\nC7aiMm8971vXgq8jcjIQc4MiIrQdsUfyPA+9s3jpaU5lI6OE1jmtT6LGkcyg/ZumWeXqcT0BWMpV\nV1UVWCv0HbXQoGgSbc+vjZp1ET7HW6+XyJJvh7sxdlLKBvDae5pSQuLt3ou3aSzrRn9waWMnjvrY\n2Hs8l8y+FtkxigyqJaozTZMvfIA5p8c52HGhfwzjAGXN7O3ocD6fcblc0Pcdunm7w/4I5xyO9RHT\nZGGnuRswSPmZObPzXPI8R9cNwTM7DAOapsHx6YC///3vaC5eENV1DaUUmrYNQqksyxCmbppmMZAy\njaqsoYYhcHLzPEfT9cBA/Tf8Obo54ZArMymP7H/6pST4PnHNeOGfEd7rCEkpDrGRwo0jYJ3EG8/h\nrRfk1rN+i7HDIzv8JQ8sNDLe9I9oJoA3Jijq0s8NgePoKkVgiYfPixbEycxkWNH3PMoTKzQ0P+dc\nkCPAmn5Dc6HEZJ4LwBO2qX8GNyZTMkSUE8H34J1PGTBfQp97776cJhY7l2PHC4+e0r6xQUDGTl3X\nyPM80FwBrKIyFD3hDmRevICOzxsZcwMmFaklxktRFNjv90GvoOuy5Wiic+bH5vKEj/3K6S5y5Jvj\nboydLdyipKw+u+EZSxlQb8wiOsbyt8Z60ceRpZVwWC3E18aVcxaWjB3GtQ9JcUrBGoNpHGGGPigs\ntCC54qLU3FRvrrA29J7y8fHpI7yt5RfpMPazN8YLoywvg9AZpsUjO04TmqYBtKeL7OsnTM7CDosC\nYYzB4XAIhQS4x8c5vx/3/LR9F7wlfT8gz7PAzydvz263Q99OQaDG11SEikAgEAgEAsGPi7s0dlIe\n15tpaZ+p+16jv8Q0Ns3mSFS1JI3NrhtoUmSHjJ3XnpN16Vlegcw5h7broJzPXdIAdJFDzYZE1zcr\nzwZ5Oqgk9fPzM56fn7Erdt7zimyO7Pj929aXeD0cn4IX9Pnc4PnlBcMwYL/fYxgG/Pu33zBME06n\nCR8/fvRGy+wF5omIWmtf/WiOiv1///ondL7UwYdToYDBfr/3CcvaJx937QAFhb4fYSbWyDW6N5vP\ngEDAEEeFY3mS+o72I2ztF28Xb781n63fU885n1Oco7N1LjyyQ9FhHr3hER5+zJjqRs2CaQwqGEIg\nah7vjs4LENB3NIZSCl3X4ddffwXgHTIfPnxYRWtScpj6c3EqIDlRaM5Es+UUO05TS9H46JoJBBzf\nyzMR59fwtZoqjPItiifEkWrukCUnZBwl4bTz/X6/yn8huUBrUylfSCk+Z1rrRF87Ho/hJ1V85BT3\nuKABfU55hzR3kmukm/ACKJQTyHvxpCj0W9FhYZ98O9yVsXONvpGK5sTbAICzaYXE/6QHVCFRCoEC\nNuzn20r0ZAx86elZKWELM4uiO261QFJ9LyyMWagunMbGKTDOORi3hE4pzEtUknEcl0S6WdkYxxHT\naPD8/OwjOFpDqxzD2MzjKjRNgywvQ14NFz7t0KPa11DNC/75z39iGBb6zDQfl45D5WeBJQG5qir8\n+fIMa1xIMLTWQhe+N09d19B5hubSwRRuDm0b5EUGM75W8FK/X4sACn4cvF5X29vEn13jtW8ZQ6mX\n2ZaBkjJmuHIQK+Nb279FnePygufs8GZ+BD4e8eV5cjCwGBZd1wVKCoBQxZH3uqFtaX+ixbVtGxSG\n5+dnAAhG0m63C3Q5Tl0dZsorjU9GD52jcy5EoqliEu0bV6GLKX9bz4nIEcG3QCovJ/4ufha3nCic\nBvalBk8qJ4XrMbyMfTxvkgU874Vvz3P5CNSfjxsRSq2bksYUtb7vwxhUKZJYISTjeL8v7nSJK7WR\nfCCjhs+DmC3xdX1LZgi+Pe7K2Pme8D294PhMnIo+QCo/gClhTIGiZORxHGdzL4Ox/axszXQ4ZuzA\nzd6QeR2TQpFlGZq2RXk+rxQdYwx4+yAbr/958lbNM1SgjkkYphHFnDdUVZWn0b2cfc8jiCARfBlu\ncZpwxAnBW9Gft45JP98yvLhCvmXApM5h6zixc4TGjvOG4uNx7yX/jIwgay1eXl5CAQMyerhiQQUL\naBxSMug88jzHy8sLAAQvbV3XwdCh43Dwv3m1NW6oxQnGZIi1bftdyXPB943/+Z//wX//93+vPotz\n4m5BymhIGS/Xxr3mJInB13ksc67JLL5tSpaQw4RHhrksIQcHz+vrus7T3oE5f3jpm5PnOY7HIz58\n+ABgKVPP6etFUQTZAiCMTbKEFxkoyzLk4pC8oXkACPtRpJfOhZ8vFTGgqmv0OW3Le/qk7suW8XMt\n+i/4ulBykQUCgUAgEAgEAsEj4nqNPIFAIBAIBAKBQCC4U4ixIxAIBAKBQCAQCB4SYuwIBAKBQCAQ\nCASCh4QYOwKBQCAQCAQCgeAhIcaOQCAQCAQCgUAgeEiIsSMQCAQCgUAgEAgeEmLsCAQCgUAgEAgE\ngoeEGDsCgUAgEAgEAoHgISHGjkAgEAgEAoFAIHhIiLEjEAgEAoFAIBAIHhJi7AgEAoFAIBAIBIKH\nhBg7AoFAIBAIBAKB4CEhxo5AIBAIBAKBQCB4SIixIxAIBAKBQCAQCB4SYuwIBAKBQCAQCASCh4QY\nOwKBQCAQCAQCgeAhIcaOQCAQCAQCgUAgeEiIsSMQCAQCgUAgEAgeEmLsCAQCgUAgEAgEgoeEGDsC\ngUAgEAgEAoHgISHGjkAgEAgEAoFAIHhIiLEjEAgEAoFAIBAIHhJi7AgEAoFAIBAIBIKHhBg7AoFA\nIBAIBAKB4CEhxo5AIBAIBAKBQCB4SIixIxAIBAKBQCAQCB4SYuwIBAKBQCAQCASCh4QYOwKBQCAQ\nCAQCgeAhIcaOQCAQCAQCgUAgeEj8/6Rpmbid8I+IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5794fbfb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.15980316116e-08\n",
      "dw error:  2.24712647485e-10\n",
      "db error:  3.3726400665e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around 1e-8'\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Max pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.16666651573e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Max pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 6.595680s\n",
      "Fast: 0.027977s\n",
      "Speedup: 235.753741x\n",
      "Difference:  4.92640785149e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 5.158938s\n",
      "Fast: 0.011594s\n",
      "Speedup: 444.954843x\n",
      "dx difference:  1.94976477535e-11\n",
      "dw difference:  5.46927446708e-13\n",
      "db difference:  3.48135461319e-14\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.432200s\n",
      "fast: 0.001995s\n",
      "speedup: 216.684078x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.504894s\n",
      "fast: 0.010947s\n",
      "speedup: 46.122773x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  6.51433656926e-09\n",
      "dw error:  1.4908476761e-08\n",
      "db error:  2.03739035622e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  3.56006101152e-09\n",
      "dw error:  2.2497685695e-10\n",
      "db error:  1.30876199758e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.30258607124\n",
      "Initial loss (with regularization):  2.50825563567\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to 1e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 1.380104e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.064049e-04\n",
      "b1 max relative error: 3.477652e-05\n",
      "b2 max relative error: 2.516375e-03\n",
      "b3 max relative error: 7.945660e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.190000; val_acc: 0.128000\n",
      "(Iteration 2 / 30) loss: 2.609504\n",
      "(Epoch 1 / 15) train acc: 0.230000; val_acc: 0.094000\n",
      "(Iteration 3 / 30) loss: 2.113380\n",
      "(Iteration 4 / 30) loss: 1.971811\n",
      "(Epoch 2 / 15) train acc: 0.310000; val_acc: 0.098000\n",
      "(Iteration 5 / 30) loss: 1.676728\n",
      "(Iteration 6 / 30) loss: 1.801782\n",
      "(Epoch 3 / 15) train acc: 0.570000; val_acc: 0.191000\n",
      "(Iteration 7 / 30) loss: 1.652683\n",
      "(Iteration 8 / 30) loss: 1.598651\n",
      "(Epoch 4 / 15) train acc: 0.570000; val_acc: 0.194000\n",
      "(Iteration 9 / 30) loss: 1.070849\n",
      "(Iteration 10 / 30) loss: 1.408982\n",
      "(Epoch 5 / 15) train acc: 0.740000; val_acc: 0.188000\n",
      "(Iteration 11 / 30) loss: 0.816042\n",
      "(Iteration 12 / 30) loss: 0.807953\n",
      "(Epoch 6 / 15) train acc: 0.820000; val_acc: 0.256000\n",
      "(Iteration 13 / 30) loss: 0.971160\n",
      "(Iteration 14 / 30) loss: 0.568949\n",
      "(Epoch 7 / 15) train acc: 0.860000; val_acc: 0.236000\n",
      "(Iteration 15 / 30) loss: 0.394380\n",
      "(Iteration 16 / 30) loss: 0.401405\n",
      "(Epoch 8 / 15) train acc: 0.910000; val_acc: 0.194000\n",
      "(Iteration 17 / 30) loss: 0.723469\n",
      "(Iteration 18 / 30) loss: 0.258560\n",
      "(Epoch 9 / 15) train acc: 0.910000; val_acc: 0.169000\n",
      "(Iteration 19 / 30) loss: 0.242372\n",
      "(Iteration 20 / 30) loss: 0.235556\n",
      "(Epoch 10 / 15) train acc: 0.940000; val_acc: 0.199000\n",
      "(Iteration 21 / 30) loss: 0.212628\n",
      "(Iteration 22 / 30) loss: 0.126721\n",
      "(Epoch 11 / 15) train acc: 0.940000; val_acc: 0.213000\n",
      "(Iteration 23 / 30) loss: 0.113127\n",
      "(Iteration 24 / 30) loss: 0.270010\n",
      "(Epoch 12 / 15) train acc: 0.970000; val_acc: 0.204000\n",
      "(Iteration 25 / 30) loss: 0.067624\n",
      "(Iteration 26 / 30) loss: 0.081088\n",
      "(Epoch 13 / 15) train acc: 1.000000; val_acc: 0.207000\n",
      "(Iteration 27 / 30) loss: 0.029606\n",
      "(Iteration 28 / 30) loss: 0.051089\n",
      "(Epoch 14 / 15) train acc: 1.000000; val_acc: 0.209000\n",
      "(Iteration 29 / 30) loss: 0.027549\n",
      "(Iteration 30 / 30) loss: 0.025578\n",
      "(Epoch 15 / 15) train acc: 1.000000; val_acc: 0.209000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=15, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7ASO8V0uSJKk52EKlqlix4upimFpEIUwBBPn8IkZGelm5cl01yyuJLW6SJEnNx0Clqtiy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\nR1WvTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57667c7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304740\n",
      "(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n",
      "(Iteration 21 / 980) loss: 2.132736\n",
      "(Iteration 41 / 980) loss: 1.890223\n",
      "(Iteration 61 / 980) loss: 1.790478\n",
      "(Iteration 81 / 980) loss: 1.745026\n",
      "(Iteration 101 / 980) loss: 1.892161\n",
      "(Iteration 121 / 980) loss: 1.890149\n",
      "(Iteration 141 / 980) loss: 1.964029\n",
      "(Iteration 161 / 980) loss: 1.741861\n",
      "(Iteration 181 / 980) loss: 1.886438\n",
      "(Iteration 201 / 980) loss: 1.958140\n",
      "(Iteration 221 / 980) loss: 1.789366\n",
      "(Iteration 241 / 980) loss: 1.693459\n",
      "(Iteration 261 / 980) loss: 1.507454\n",
      "(Iteration 281 / 980) loss: 1.622408\n",
      "(Iteration 301 / 980) loss: 1.845511\n",
      "(Iteration 321 / 980) loss: 1.737180\n",
      "(Iteration 341 / 980) loss: 1.680309\n",
      "(Iteration 361 / 980) loss: 1.749498\n",
      "(Iteration 381 / 980) loss: 1.420701\n",
      "(Iteration 401 / 980) loss: 1.685015\n",
      "(Iteration 421 / 980) loss: 1.733135\n",
      "(Iteration 441 / 980) loss: 1.591582\n",
      "(Iteration 461 / 980) loss: 1.700545\n",
      "(Iteration 481 / 980) loss: 1.492935\n",
      "(Iteration 501 / 980) loss: 1.404087\n",
      "(Iteration 521 / 980) loss: 1.712702\n",
      "(Iteration 541 / 980) loss: 1.459696\n",
      "(Iteration 561 / 980) loss: 1.607686\n",
      "(Iteration 581 / 980) loss: 1.232315\n",
      "(Iteration 601 / 980) loss: 1.641778\n",
      "(Iteration 621 / 980) loss: 1.560701\n",
      "(Iteration 641 / 980) loss: 1.642898\n",
      "(Iteration 661 / 980) loss: 1.651685\n",
      "(Iteration 681 / 980) loss: 1.799905\n",
      "(Iteration 701 / 980) loss: 1.447530\n",
      "(Iteration 721 / 980) loss: 1.538806\n",
      "(Iteration 741 / 980) loss: 1.893407\n",
      "(Iteration 761 / 980) loss: 1.408911\n",
      "(Iteration 781 / 980) loss: 2.160169\n",
      "(Iteration 801 / 980) loss: 1.850402\n",
      "(Iteration 821 / 980) loss: 1.542580\n",
      "(Iteration 841 / 980) loss: 1.441510\n",
      "(Iteration 861 / 980) loss: 1.779246\n",
      "(Iteration 881 / 980) loss: 1.656049\n",
      "(Iteration 901 / 980) loss: 1.455208\n",
      "(Iteration 921 / 980) loss: 1.820103\n",
      "(Iteration 941 / 980) loss: 1.526195\n",
      "(Iteration 961 / 980) loss: 1.639982\n",
      "(Epoch 1 / 1) train acc: 0.484000; val_acc: 0.485000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ideWyew1lIa0XVZqZFZvOyTza+U6Zr6cuuvcYTOqiZn1RP9fbDV1I3YnFfr0Y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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57668220b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 9.33463814  8.90909116  9.11056338]\n",
      "  Stds:  [ 3.61447857  3.19347686  3.5168142 ]\n",
      "After spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [  6.18949336e-16   5.99520433e-16  -1.22124533e-16]\n",
      "  Stds:  [ 0.99999962  0.99999951  0.9999996 ]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 6.  7.  8.]\n",
      "  Stds:  [ 2.99999885  3.99999804  4.99999798]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [-0.08034406  0.07562881  0.05716371  0.04378383]\n",
      "  stds:  [ 0.96718744  1.0299714   1.02887624  1.00585577]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  2.78664819387e-07\n",
      "dgamma error:  7.09748171136e-12\n",
      "dbeta error:  3.27560872528e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
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